Detecting RNA fusions in cancer - the benefits, the challenges and the solution

RNA sequencing (RNA-Seq) of oncogenic fusions is becoming increasingly popular in cancer research and diagnosis. Here, we talk about the benefits of using RNA in fusion detection workflows, the challenge of workflow variability and how to be confident of your assay results.

Why choose RNA for diagnostics?

RNA has an essential role in many biological processes. So, when it is used for molecular diagnostics, it has the potential to be used widely in human health. The most prominent use is in disease diagnosis, prognosis and therapeutic selection.

Benefits Challenges
It's a functional entity Unstable
Localization information is important Difficult to extract from FFPE
Allows multiplexing Expression levels may vary
Can be used for liquid biopsies   
Tells the state of a biological system

The evolution of next-generation sequencing (NGS) has enabled scientists to sequence RNA (RNA-Seq) using this new technology, giving us an in-depth view of the transcriptome and means we can detect novel RNA transcript variations. This method of sequencing is proven to be more flexible, sensitive and accurate than older methods, such as PCR and FISH. There are several advantages to using RNA-Seq in place of gene expression microarrays, including:

  • Quantification of an increased dynamic range of expression
  • Detection of all SNVs, insertions and deletions (microarrays have defined probes designed to detect only certain transcripts / variants)
  • Detection of different transcript isoforms, splice variants and chimeric gene fusions
  • It can be performed on any species (microarrays have species-specific probes)
Why detect for RNA fusions?

The analysis of RNA is an invaluable tool for the investigation of cancer samples and is increasingly used in clinical diagnostics.

Understanding changes in gene expression and gene regulation, as well as identifying gene fusions that arise from chromosomal translocations, can help researchers and clinicians to detect key driver mutations that are present in the tumor.

There are already well-established gene fusion associations with specific cancer types. For example, the AML-ETA fusion has been used for the detection of acute myeloid leukemia since 20081. There is also potential for fusion detection to be used in prognosis and as biomarkers for screening and assessment of cancer risk2.

RNA-Seq is a highly accurate method and offers both qualitative and quantitative information. It allows for expression analysis and identification of fusion genes.

One of the main reasons this technique is becoming increasingly popular and attractive in clinical diagnostics, is the ability to take one tumor sample, carry out RNA-Seq and characterize it for multiple genotypes.

Common applications of this method include:

  • Profiling expression of select target genes to assess disease-associated variants and epigenetic alterations
  • Analyzing gene fusions and gene expression alterations to provide a focused view of functionally relevant changes occurring in cancer
The challenges

RNA-Seq can potentially lead to false-negative results. Poor quality RNA will decrease the number and quality of reads, which reduces the likelihood of detecting fusion transcripts. Also, gene fusions that are expressed at low levels, or result in transcriptional silencing of a gene, can be extremely difficult to detect. However, these challenges can be counteracted by combining RNA-Seq with whole-genome DNA sequencing.

Other common challenges in RNA fusion detection:

  • Limited or poor quality material
  • These materials are often heterogenous, consisting of multiple cell populations and even multiple sub-clones of cancer
  • No good extraction control
  • Failed RNA extraction
A closer look at variation in the workflow

Variation can creep in at every part of this workflow, and where it does, it will impact the output – your results.

RNA-based diagnostic workflows are made of pre-analytical and analytical procedures. The molecular assay output and following diagnosis are all dependent on the correct functioning of the preceding steps. For example, during extraction, there is always a balance to strike between the concentration, yield and quality.

RNA quantification is also a critical step in the workflow. Whether you quantify RNA by a metric measurement or spectrophotometer, you can get a different answer – which could under or over represent the amount of RNA you have. Over-estimating the RNA quantity can lead to under-loading the library preparation step, this could then cause a cascade of problems during analysis.

So, this leaves us with some questions:

  • What is the best kit for me to do an RNA extraction?
  • What is the best method for RNA quantification for our lab?
  • Is there any library prep bias?
  • Is there any bias in RNA-Seq?
Answering your variability questions by using Reference Standards

Reference standards will help you answer some of these questions. Using reference standards allows you to validate, optimize and routinely monitor the performance of your assay. They enable the measurement of the sensitivity, specificity and accuracy of your workflow.

Reference standards can be applied to assess analytical variability and reproducibility between platforms, laboratories, operators and assays.

Using reference standards gives you:

  • Confidence in your clinical workflow, from extraction to analysis
  • Evaluation of your workflow integrity from pre-analytical RNA extraction through to fusion detection
  • Optimization and validation of new targeted RNA panels
  • Routine monitoring of the performance of your assay
  • Assessment of the performance of RNA-Seq, endpoint RT-PCR or RT-qPCR aimed at detecting gene fusions
  1. Vardiman, J. W. et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood 114, 937–951 (2009).
  2. Font-Tello, A. et al. Association of ERG and TMPRSS2‑ERG with grade, stage, and prognosis of prostate cancer is dependent on their expression levels. Prostate 75, 1216–1226 (2015).